{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "# 加载数据\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "# 返回 numpy arrays，train_images shape是(6000,28,28)，train_labels shape是(6000,)，并且取值范围在[0,9]-R,test_image shape是(1000,28,28)，test_label是(6000,)\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "print(train_labels.shape)\n",
    "# 该数据集包含0-9标签，分别表示以下类型\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预处理数据\n",
    "```python\n",
    "# 观测图片，以第一张图片为例，可以看到该图片的像素的值大小在[0,255]\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "```\n",
    "所以可以将像素的值缩小到0到1之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "# 观察转换后的图像\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 5s 82us/sample - loss: 0.4954 - accuracy: 0.8262\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 5s 79us/sample - loss: 0.3702 - accuracy: 0.8669\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 75us/sample - loss: 0.3345 - accuracy: 0.8777\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.3131 - accuracy: 0.8857\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.2954 - accuracy: 0.8902\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.2823 - accuracy: 0.8949\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 5s 78us/sample - loss: 0.2685 - accuracy: 0.9006\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 5s 76us/sample - loss: 0.2585 - accuracy: 0.9051\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.2477 - accuracy: 0.9083\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 6s 97us/sample - loss: 0.2384 - accuracy: 0.9114\n",
      "10000/10000 - 1s - loss: 0.3334 - accuracy: 0.8853\n",
      "Test accuracy is: 0.8853\n",
      "predition label is: 9 , the really label is: 9\n"
     ]
    }
   ],
   "source": [
    "# 1.创建模型结构\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)), # 转换数据格式，即(28, 28)-->(28 x 28 = 784,)\n",
    "    keras.layers.Dense(128, activation='relu'), # 经过128个节点，激活函数为relu\n",
    "    keras.layers.Dense(10) # 返回长度为10的logits数组\n",
    "])\n",
    "# 2.编译模型：在模型开始训练之前，需要进行一些设置，包括损失函数、优化器、指标。\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "# 3.训练模型\n",
    "# 3.1 将训练数据填充入模型\n",
    "model.fit(train_images, train_labels, epochs = 10)\n",
    "# 3.2 评估精确值\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose = 2)\n",
    "print('Test accuracy is:', test_acc) # 测试数据集的准确性略低于训练数据集的准确性，代表过渡拟合\n",
    "# 3.3 做出预测\n",
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])\n",
    "predictions = probability_model.predict(test_images)\n",
    "print('predition label is:', np.argmax(predictions[0]), ', the really label is:', test_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以绘图的方式显示对物品的预测\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the predition label is: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.4 验证预测\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# 4. 使用模型\n",
    "img = test_images[1]\n",
    "img = (np.expand_dims(img,0))\n",
    "predictions_single = probability_model.predict(img)\n",
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "print('the predition label is:', np.argmax(predictions_single[0]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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